Literature DB >> 28654965

Mid-level perceptual features contain early cues to animacy.

Bria Long1, Viola S Störmer2, George A Alvarez3.   

Abstract

While substantial work has focused on how the visual system achieves basic-level recognition, less work has asked about how it supports large-scale distinctions between objects, such as animacy and real-world size. Previous work has shown that these dimensions are reflected in our neural object representations (Konkle & Caramazza, 2013), and that objects of different real-world sizes have different mid-level perceptual features (Long, Konkle, Cohen, & Alvarez, 2016). Here, we test the hypothesis that animates and manmade objects also differ in mid-level perceptual features. To do so, we generated synthetic images of animals and objects that preserve some texture and form information ("texforms"), but are not identifiable at the basic level. We used visual search efficiency as an index of perceptual similarity, as search is slower when targets are perceptually similar to distractors. Across three experiments, we find that observers can find animals faster among objects than among other animals, and vice versa, and that these results hold when stimuli are reduced to unrecognizable texforms. Electrophysiological evidence revealed that this mixed-animacy search advantage emerges during early stages of target individuation, and not during later stages associated with semantic processing. Lastly, we find that perceived curvature explains part of the mixed-animacy search advantage and that observers use perceived curvature to classify texforms as animate/inanimate. Taken together, these findings suggest that mid-level perceptual features, including curvature, contain cues to whether an object may be animate versus manmade. We propose that the visual system capitalizes on these early cues to facilitate object detection, recognition, and classification.

Mesh:

Year:  2017        PMID: 28654965     DOI: 10.1167/17.6.20

Source DB:  PubMed          Journal:  J Vis        ISSN: 1534-7362            Impact factor:   2.240


  17 in total

1.  Mid-level visual features underlie the high-level categorical organization of the ventral stream.

Authors:  Bria Long; Chen-Ping Yu; Talia Konkle
Journal:  Proc Natl Acad Sci U S A       Date:  2018-08-31       Impact factor: 11.205

2.  Visual segmentation of complex naturalistic structures in an infant eye-tracking search task.

Authors:  Karola Schlegelmilch; Annie E Wertz
Journal:  PLoS One       Date:  2022-04-01       Impact factor: 3.240

3.  Mid-level Feature Differences Support Early Animacy and Object Size Distinctions: Evidence from Electroencephalography Decoding.

Authors:  Ruosi Wang; Daniel Janini; Talia Konkle
Journal:  J Cogn Neurosci       Date:  2022-08-01       Impact factor: 3.420

4.  Curvature processing in human visual cortical areas.

Authors:  Xiaomin Yue; Sophia Robert; Leslie G Ungerleider
Journal:  Neuroimage       Date:  2020-08-21       Impact factor: 6.556

5.  Animals Do Not Induce or Reduce Attentional Blinking, But They Are Reported More Accurately in a Rapid Serial Visual Presentation Task.

Authors:  Thomas Hagen; Bruno Laeng
Journal:  Iperception       Date:  2017-10-16

6.  Roles of Category, Shape, and Spatial Frequency in Shaping Animal and Tool Selectivity in the Occipitotemporal Cortex.

Authors:  Chenxi He; Shao-Chin Hung; Olivia S Cheung
Journal:  J Neurosci       Date:  2020-06-11       Impact factor: 6.167

7.  Humans can decipher adversarial images.

Authors:  Zhenglong Zhou; Chaz Firestone
Journal:  Nat Commun       Date:  2019-03-22       Impact factor: 14.919

8.  Ultra-rapid object categorization in real-world scenes with top-down manipulations.

Authors:  Bingjie Xu; Mohan S Kankanhalli; Qi Zhao
Journal:  PLoS One       Date:  2019-04-10       Impact factor: 3.240

9.  Topography of Visual Features in the Human Ventral Visual Pathway.

Authors:  Shijia Fan; Xiaosha Wang; Xiaoying Wang; Tao Wei; Yanchao Bi
Journal:  Neurosci Bull       Date:  2021-07-02       Impact factor: 5.271

10.  Bottom-up processing of curvilinear visual features is sufficient for animate/inanimate object categorization.

Authors:  Valentinos Zachariou; Amanda C Del Giacco; Leslie G Ungerleider; Xiaomin Yue
Journal:  J Vis       Date:  2018-11-01       Impact factor: 2.240

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